At least some known businesses typically collect large amounts of data and use business intelligence and analytics to make sense of the data. As the volume of data being generated and analyzed has increased, so has the need for analyzing this data in real-time. This increasingly data rich environment offers tremendous potential for improving business results by making increasingly data driven decisions. This same growth in data volume, and the growing need to make more decisions more rapidly, is pushing businesses to look at new approaches and technology. Many businesses are adopting techniques, such as predictive analytics, to extract more meaning from their data. These techniques process large amounts of historical data to make predictions about the future. They allow businesses to make probabilistic predictions, such as how likely is this transaction to be fraudulent, how loyal is this customer, what offer will be most effective at increasing basket size for these customers and more. These predictions and probabilities can be used to improve decision-making throughout the organization.
Predictive analytics provides an enhanced view of customers and makes predictions about their current and future transaction behavior. This technique can be applied to a myriad of challenges, including customer segmentation, merchandising optimization, information security, marketplace trust and safety, and buyer fraud. However, within many businesses, the promise of predictive analytics is inhibited by a misalignment between technical and data science resources.
Customers of a business entity may buy, sell, or otherwise interact with the business entity's online transaction system through a website or a mobile application. The resulting online transaction data may be stored in a production database and first extracted, transformed, and loaded (ETL) into a data warehouse. A data scientist may take the transformed data and use different statistical tools to build an analysis (e.g., an algorithm or analytical process) in the form of models and scores. Examples of the statistical tools that the data scientist may use include R™ SaS™, MATLAB™, and the like.
Once the data scientist builds an analysis (e.g., an analytical machine learning (ML) model), the analysis is released to an application developer that must also convert the analysis written with one of the statistical tools into a language used in the online transaction system. The resulting code is then released into the online transaction system. However, the involvement of both the data scientist and the application developer creates a life cycle issue as the data scientist and the application developer will coordinate to properly release the data scientist's analysis upon the online transaction system. Also, the data scientist and application developer may iterate to avoid a mismatch between the application developer's interpretation and the data scientist's intention for an analysis. Accordingly, conventional techniques to produce analyses may require large amounts of overhead, but still fail to produce satisfactory results. Therefore, conventional techniques to for analysis production (e.g., producing an ML model) are not entirely satisfactory.